1. Field of the Invention
This invention relates to methods and systems inferring one or more biochemical interaction networks, including topology and chemical reaction rates and parameters, from experimental data and a database of possible interactions.
2. Description of the Related Art
Recent advances in bioinformatics have provided an extensive amount of statistical data on different aspects of cellular biology, including gene expression data. This statistical data has allowed for relocating the study of biology from a traditional laboratory setting involving test tubes and microscopes to a different kind of laboratory, consisting mainly of computer terminals. This new area of study is often referred to as in silico biology. A recent area of focus of in silico biology has been the development of computer models simulating and predicting genomic behavior, such as the study of drug targets for the remediation of a given disease condition. Previously, computer simulated biochemical network models were subject to constraints due to the limited availability of experimental data. However, recent developments in high-throughput methodologies able to rapidly amass large quantities of genomic data are making data constraints a problem of the past.
As increasing amounts of genomic-scale data becomes available, a new problem arises in trying to translate large stores of information into a useful, digestible form that enables accurate predictions of cell behavior. Currently, researchers must return to the lab each time they encounter an unknown variable, such as the presence or absence of a link in a particular disease pathway. This limits the efficiency of drug discovery, and requires researchers to make assumptions not accurately reflecting the complex nature of the biochemical interaction network underlying biological process. With in silico, or computer-simulated, biochemical interaction networks, a researcher may quickly predict the effects of unknowns on disease pathways and drug targets without leaving the comfort of their desks.
Unfortunately, the limited availability of genomic data was not the only constraint on the efforts to create accurate network models. Rather, a limited knowledge of the complex biochemical circuitry at the cellular level remains a real obstacle to the predictive accuracy of any in silico biological model. Thus, there exists a need for identifying, modeling or otherwise accounting for the manner in which cells operate.